Jäschke,Robert
Rudolph,Sebastian
Attribute Exploration on the Web
19–34
2013
We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.
Doerfel,Stephan
Jäschke,Robert
Stumme,Gerd
Publication Analysis of the Formal Concept Analysis Community
Springer
7278
77–95
2012
We present an analysis of the publication and citation networks of all previous editions of the three conferences most relevant to the FCA community: ICFCA, ICCS and CLA. Using data mining methods from FCA and graph analysis, we investigate patterns and communities among authors, we identify and visualize influential publications and authors, and we give a statistical summary of the conferences’ history.
Poelmans,Jonas
Ignatov,DmitryI.
Viaene,Stijn
Dedene,Guido
Kuznetsov,SergeiO.
Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research
Springer Berlin Heidelberg
7377
273-287
2012
Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
Hotho,Andreas
Jäschke,Robert
Schmitz,Christoph
Stumme,Gerd
Information Retrieval in Folksonomies: Search and Ranking
Springer
411-426
2006
Cimiano,Philipp
Hotho,Andreas
Stumme,Gerd
Tane,Julien
Conceptual Knowledge Processing with Formal Concept
Analysis and Ontologies
Springer
2961
189–207
2004
Calders,Toon
Goethals,Bart
Mining All Non-derivable Frequent Itemsets
Springer-Verlag
74–85
2002
Stumme,Gerd
Taouil,Rafik
Bastide,Yves
Pasquier,Nicolas
Lakhal,Lotfi
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Elsevier Science Publishers B. V.
42
189–222
2002
We introduce the notion of iceberg concept lattices
and show their use in knowledge discovery in
databases. Iceberg lattices are a conceptual
clustering method, which is well suited for analyzing
very large databases. They also serve as a condensed
representation of frequent itemsets, as starting
point for computing bases of association rules, and
as a visualization method for association rules.
Iceberg concept lattices are based on the theory of
Formal Concept Analysis, a mathematical theory with
applications in data analysis, information retrieval,
and knowledge discovery. We present a new algorithm
called TITANIC for computing (iceberg) concept
lattices. It is based on data mining techniques with
a level-wise approach. In fact, TITANIC can be used
for a more general problem: Computing arbitrary
closure systems when the closure operator comes along
with a so-called weight function. The use of weight
functions for computing closure systems has not been
discussed in the literature up to now. Applications
providing such a weight function include association
rule mining, functional dependencies in databases,
conceptual clustering, and ontology engineering. The
algorithm is experimentally evaluated and compared
with Ganter's Next-Closure algorithm. The evaluation
shows an important gain in efficiency, especially for
weakly correlated data.
Burdick,D.
Calimlim,M.
Gehrke,J.
MAFIA: A maximal frequent itemset algorithm for transactional databases
IEEE Computer Society
2001
Bykowski,Artur
Rigotti,Christophe
A condensed representation to find frequent patterns.
2001
Bastide,Y.
Data Mining: algorithmes par niveau, techniques d'implementation
et applications
2000
Boulicaut,Jean-Francois
Bykowski,Artur
Rigotti,Christophe
Approximation of Frequency Queries by Means of Free-Sets
Springer-Verlag
75–85
2000
Burmeister,P.
Formal concept analysis with : Introduction to the
basic features
1998
Fayet,A.
Giacometti,A.
Laurent,D.
Spyratos,N.
D'ecouverte de r�gles pertinentes dans les bases de donn�es
197–211
1998
Brin,S.
Motwani,R.
Silverstein,C.
Beyond market baskets: Generalizing association rules to correlation
ACM Press
265–276
1997
Brin,S.
Motwani,R.
Ullman,J.D.
Tsur,S.
Dynamic itemset counting and implication rules for market basket
data
ACM Press
255–264
1997
Fayyad,U.M.
Piatetsky-Shapiro,G.
Smyth,P.
From data mining to knowledge discovery : An overview
AAAI Press
1–30
1996
Fayyad,U.M.
Piatetsky-Shapiro,G.
Smyth,P.
Knowledge discovery and data mining : Towards a unifying framework
AAAI Press
82–88
1996
Feldman,R.
Dagan,I.
Knowledge discovery in textual databases
AAAI Press
112–117
1995
Breiman,L.
Friedman,J.H.
Olshen,R.A.
Stone,C.J.
Classification and regression trees
Wadsworth Publishing Company
1984
Brugger,W.
Philosophisches W�rterbuch
Herder
1976